Organizers: Tom Hanika, Dominik Dürrschnabel, Johannes Hirth
Duration: Half-day
The concept of order (i.e., partial ordered sets) is predominant for perceiving and organizing our physical and social environment, for inferring meaning and explanation from observation, and for searching and rectifying decisions. Compared to metric methods, however, the number of (purely) ordinal methods for capturing knowledge from data is rather small, although in principle they may allow for more comprehensible explanations. The reason for this could be the limited availability of computing resources in the last century, which would have been required for (purely) ordinal computations. Hence, typically relational and especially ordinal data are first embedded in metric spaces for learning. Therefore, in this workshop we want to collect and discuss ordinal methods for capturing and representing knowledge, their role in inference and explainability, and their possibilities for knowledge visualization and communication. We want to reflect on these topics in a broad sense, i.e., as a tool to arrange, compare and compute ontologies or concept hierarchies, as a feature in learning and capturing knowledge, and as a performance measure to evaluate model performance.